{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import transforms, utils\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.optim as optim\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import torch.utils.data as Data\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from skimage import io\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(self, in_planes, planes, stride=1):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "\n",
    "        self.shortcut = nn.Sequential()\n",
    "        if stride != 1 or in_planes != self.expansion*planes:\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(self.expansion*planes)\n",
    "            )\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.bn2(self.conv2(out))\n",
    "        out += self.shortcut(x)\n",
    "        out = F.relu(out)\n",
    "        return out\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResNet(nn.Module):\n",
    "    def __init__(self, block, num_blocks, num_output=4):\n",
    "        super(ResNet,self).__init__()\n",
    "        self.in_planes = 64\n",
    "        \n",
    "        self.conv1 = nn.Conv2d(3,64,kernel_size=3, stride=1, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)\n",
    "        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n",
    "        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n",
    "        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)\n",
    "        #self.linear = nn.Linear(512*block.expansion, num_output) \n",
    "        self.linear = nn.Linear(153600, num_output)     \n",
    "\n",
    "        \n",
    "    def _make_layer(self, block, planes, num_blocks, stride):\n",
    "        strides = [stride] + [1]*(num_blocks-1)\n",
    "        layers = []\n",
    "        for stride in strides:\n",
    "            layers.append(block(self.in_planes, planes, stride))\n",
    "            self.in_planes = planes * block.expansion\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.layer1(out)\n",
    "        out = self.layer2(out)\n",
    "        out = self.layer3(out)\n",
    "        out = self.layer4(out)\n",
    "        out = F.avg_pool2d(out, 4)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DroNet(nn.Module):\n",
    "    def __init__(self,block,num_output=4):\n",
    "        super(DroNet,self).__init__()\n",
    "        self.in_planes = 32\n",
    "        \n",
    "        self.conv1 = nn.Conv2d(3,32,kernel_size=5, stride=2, padding=1, bias=False)\n",
    "        self.pool = nn.MaxPool2d(3,stride=2)\n",
    "        self.bn1 = nn.BatchNorm2d(32)\n",
    "        self.layer1 = self._make_layer(block,32,stride=2)\n",
    "        self.layer2 = self._make_layer(block,64,stride=2)\n",
    "        self.layer3 = self._make_layer(block,128,stride=2)\n",
    "        self.linear1 = nn.Linear(1920, 400)\n",
    "        self.linear2 = nn.Linear(400, 200)\n",
    "        self.linear3 = nn.Linear(200, 100)\n",
    "        self.linear4 = nn.Linear(100, num_output)\n",
    "        \n",
    "    def _make_layer(self, block, planes, stride=2):\n",
    "        layer = block(self.in_planes, planes, stride)\n",
    "        self.in_planes = planes*block.expansion\n",
    "\n",
    "        return nn.Sequential(layer)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.layer1(out)\n",
    "        out = self.layer2(out)\n",
    "        out = self.layer3(out)\n",
    "        out = F.avg_pool2d(out, 4)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear1(out)\n",
    "        out = self.linear2(out)\n",
    "        out = self.linear3(out)\n",
    "        out = self.linear4(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResNet18():\n",
    "    return ResNet(BasicBlock, [2,2,2,2])\n",
    "def image_loader(image_name):\n",
    "    image = Image.open(image_name).convert('RGB')\n",
    "    image = image.resize((320,240))\n",
    "    return image\n",
    "transform = transforms.ToTensor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open('/home/colin/results/3000.jpg')\n",
    "image = image.resize((320,240))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=320x240 at 0x7FEA590059B0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "#device = 'cpu'\n",
    "start_epoch = 0\n",
    "#net = ResNet18().to(device)\n",
    "net = DroNet(BasicBlock).to(device)\n",
    "if device == 'cuda':\n",
    "    net = torch.nn.DataParallel(net)\n",
    "    cudnn.benchmark = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GateDataset(Dataset):\n",
    "    def __init__(self, txt, transform, loader,train=True):\n",
    "        f=open(txt, 'r')\n",
    "        data = []\n",
    "        count = 0\n",
    "        for line in f:\n",
    "            line = line.strip('\\n')\n",
    "            line = line.rstrip()\n",
    "            words = line.split()\n",
    "            label = np.array([float(words[1]),float(words[2]),float(words[3]),float(words[4])])\n",
    "            label = torch.from_numpy(label)\n",
    "            if train:\n",
    "                #if (count <= 9000):\n",
    "                if (count%5!=0):\n",
    "                    data.append((words[0],label))\n",
    "                count+=1\n",
    "            else:\n",
    "                #if (count > 9000):\n",
    "                if (count%5==0):\n",
    "                    data.append((words[0],label))\n",
    "                count+=1\n",
    "        self.data = data\n",
    "        self.transform = transform\n",
    "        self.loader = loader\n",
    "        \n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        img_file, label = self.data[index]\n",
    "        #img_file = './gate_img/'+img_file\n",
    "        img_file = '/home/colin/results/'+img_file\n",
    "        img = self.loader(img_file)\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, label\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.data)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = GateDataset(txt='/home/colin/results/ground_truth.txt',transform=transforms.ToTensor(),loader=image_loader,train=True)\n",
    "test_data = GateDataset(txt='/home/colin/results/ground_truth.txt',transform=transforms.ToTensor(),loader=image_loader,train=False)\n",
    "data_loader = DataLoader(train_data, batch_size=100,shuffle=True)\n",
    "data_loader_test = DataLoader(test_data, batch_size=100,shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96\n",
      "torch.Size([100, 3, 240, 320])\n"
     ]
    }
   ],
   "source": [
    "print(len(data_loader))\n",
    "images, labels = next(iter(data_loader))\n",
    "print(images.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96\n",
      "torch.Size([100, 3, 240, 320])\n",
      "torch.Size([100, 32, 120, 160])\n",
      "torch.Size([100, 32, 60, 80])\n",
      "torch.Size([100, 64, 30, 40])\n",
      "torch.Size([100, 128, 15, 20])\n",
      "torch.Size([100, 128, 3, 5])\n",
      "torch.Size([100, 1920])\n",
      "torch.Size([100, 4])\n"
     ]
    }
   ],
   "source": [
    "global in_planes\n",
    "in_planes = 32\n",
    "def _make_layer(block, planes, stride=2):\n",
    "    global in_planes\n",
    "    layer = block(in_planes, planes, stride)\n",
    "    in_planes = planes*block.expansion\n",
    "    return nn.Sequential(layer)\n",
    "\n",
    "print(len(data_loader))\n",
    "images, labels = next(iter(data_loader))\n",
    "print(images.size())\n",
    "conv1 = nn.Conv2d(3,32,kernel_size=5, stride=2, padding=2, bias=False)\n",
    "bn1 = nn.BatchNorm2d(32)\n",
    "out = F.relu(bn1(conv1(images)))\n",
    "layer1 = _make_layer(BasicBlock,32,stride=2)\n",
    "print(out.size())\n",
    "out = layer1(out)\n",
    "print(out.size())\n",
    "layer2 = _make_layer(BasicBlock,64,stride=2)\n",
    "out = layer2(out)\n",
    "print(out.size())\n",
    "layer3 = _make_layer(BasicBlock,128,stride=2)\n",
    "out = layer3(out)\n",
    "print(out.size())\n",
    "out = F.avg_pool2d(out, 4)\n",
    "print(out.size())\n",
    "out = out.view(out.size(0), -1)\n",
    "print(out.size())\n",
    "linear1 = nn.Linear(1920, 400)\n",
    "linear2 = nn.Linear(400, 200)\n",
    "linear3 = nn.Linear(200, 100)\n",
    "linear4 = nn.Linear(100, 4)\n",
    "out = linear1(out)\n",
    "out = linear2(out)\n",
    "out = linear3(out)\n",
    "out = linear4(out)\n",
    "\n",
    "print(out.size())\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96\n",
      "torch.Size([2, 3, 240, 320])\n",
      "torch.Size([2, 64, 240, 320])\n",
      "torch.Size([2, 128, 120, 160])\n",
      "torch.Size([2, 256, 60, 80])\n",
      "torch.Size([2, 512, 30, 40])\n",
      "torch.Size([2, 512, 7, 10])\n",
      "torch.Size([2, 35840])\n",
      "torch.Size([2, 4])\n"
     ]
    }
   ],
   "source": [
    "num_block = [2,2,2,2]\n",
    "global in_planes\n",
    "in_planes = 64\n",
    "def _make_layer(block, planes, num_blocks, stride):\n",
    "    strides = [stride] + [1]*(num_blocks-1)\n",
    "    layers = []\n",
    "    global in_planes\n",
    "    for stride in strides:\n",
    "        layers.append(block(in_planes, planes, stride))\n",
    "        in_planes = planes * block.expansion\n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "\n",
    "\n",
    "print(len(data_loader))\n",
    "#images, labels = next(iter(data_loader))\n",
    "images = torch.randn(2,3,240,320)\n",
    "print(images.size())\n",
    "conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n",
    "bn1 = nn.BatchNorm2d(64)\n",
    "out = F.relu(bn1(conv1(images)))\n",
    "layer1 = _make_layer(BasicBlock,64,num_block[0],stride=1)\n",
    "\n",
    "out = layer1(out)\n",
    "print(out.size())\n",
    "layer2 = _make_layer(BasicBlock,128,num_block[1],stride=2)\n",
    "out = layer2(out)\n",
    "print(out.size())\n",
    "layer3 = _make_layer(BasicBlock,256,num_block[2],stride=2)\n",
    "out = layer3(out)\n",
    "print(out.size())\n",
    "layer4 = _make_layer(BasicBlock,512,num_block[3],stride=2)\n",
    "out = layer4(out)\n",
    "print(out.size())\n",
    "out = F.avg_pool2d(out, 4)\n",
    "print(out.size())\n",
    "out = out.view(out.size(0), -1)\n",
    "print(out.size())\n",
    "linear = nn.Linear(35840, 4)\n",
    "out = linear(out)\n",
    "print(out.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9, weight_decay=5e-4)\n",
    "#optimizer = torch.optim.SGD(net.parameters(), lr=0.005)\n",
    "scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 150], gamma=0.1, last_epoch=-1)\n",
    "#optimizer = torch.optim.SGD(net.parameters(), lr=0.01)\n",
    "#criterion = nn.SmoothL1Loss()\n",
    "criterion = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch, net):\n",
    "    #print('\\nEpoch: {} ==> lr: {}'.format(epoch, scheduler.get_last_lr()))\n",
    "    net.train()\n",
    "    train_loss = 0\n",
    "    for batch_idx, (inputs, targets) in enumerate(data_loader):\n",
    "        inputs = torch.tensor(inputs, dtype=torch.float32).to(device)\n",
    "        targets = torch.tensor(targets, dtype=torch.float32).to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "        train_loss = (train_loss/(batch_idx+1))\n",
    "        print(\"the trainning loss is (%-10.3f): \" %(train_loss))\n",
    "def test(epoch, net):\n",
    "    net.eval()\n",
    "    test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, (inputs, targets) in enumerate(data_loader_test):\n",
    "            inputs = torch.tensor(inputs, dtype=torch.float32).to(device)\n",
    "            targets = torch.tensor(targets, dtype=torch.float32).to(device)\n",
    "            outputs = net(inputs) \n",
    "            loss = criterion(outputs, targets)\n",
    "            test_loss += loss.item()\n",
    "            test_loss = test_loss/(batch_idx+1)\n",
    "            print(\"the test loss is (%-10.3f): \" %(test_loss))\n",
    "\n",
    "\n",
    "\n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Training parameters:\n",
      "        start_epoch = 1\n",
      "        lr @epoch=0 = 0.1\n",
      "==> Starting training...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/ipykernel_launcher.py:6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  \n",
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the trainning loss is (1.220     ): \n",
      "the trainning loss is (0.886     ): \n",
      "the trainning loss is (0.691     ): \n",
      "the trainning loss is (0.686     ): \n",
      "the trainning loss is (0.324     ): \n",
      "the trainning loss is (0.167     ): \n",
      "the trainning loss is (0.109     ): \n",
      "the trainning loss is (0.194     ): \n",
      "the trainning loss is (0.147     ): \n",
      "the trainning loss is (0.121     ): \n",
      "the trainning loss is (0.141     ): \n",
      "the trainning loss is (0.161     ): \n",
      "the trainning loss is (0.068     ): \n",
      "the trainning loss is (0.145     ): \n",
      "the trainning loss is (0.098     ): \n",
      "the trainning loss is (0.047     ): \n",
      "the trainning loss is (0.087     ): \n",
      "the trainning loss is (0.050     ): \n",
      "the trainning loss is (0.043     ): \n",
      "the trainning loss is (0.055     ): \n",
      "the trainning loss is (0.048     ): \n",
      "the trainning loss is (0.034     ): \n",
      "the trainning loss is (0.058     ): \n",
      "the trainning loss is (0.037     ): \n",
      "the trainning loss is (0.029     ): \n",
      "the trainning loss is (0.035     ): \n",
      "the trainning loss is (0.042     ): \n",
      "the trainning loss is (0.032     ): \n",
      "the trainning loss is (0.023     ): \n",
      "the trainning loss is (0.020     ): \n",
      "the trainning loss is (0.039     ): \n",
      "the trainning loss is (0.030     ): \n",
      "the trainning loss is (0.039     ): \n",
      "the trainning loss is (0.022     ): \n",
      "the trainning loss is (0.030     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.040     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.020     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.022     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.035     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.020     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.028     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.026     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.012     ): \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/ipykernel_launcher.py:21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/ipykernel_launcher.py:22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the test loss is (2.032     ): \n",
      "the test loss is (2.449     ): \n",
      "the test loss is (1.174     ): \n",
      "the test loss is (1.263     ): \n",
      "the test loss is (0.738     ): \n",
      "the test loss is (0.400     ): \n",
      "the test loss is (0.389     ): \n",
      "the test loss is (0.157     ): \n",
      "the test loss is (0.216     ): \n",
      "the test loss is (0.125     ): \n",
      "the test loss is (0.340     ): \n",
      "the test loss is (0.136     ): \n",
      "the test loss is (0.223     ): \n",
      "the test loss is (0.178     ): \n",
      "the test loss is (0.141     ): \n",
      "the test loss is (0.070     ): \n",
      "the test loss is (0.057     ): \n",
      "the test loss is (0.081     ): \n",
      "the test loss is (0.147     ): \n",
      "the test loss is (0.085     ): \n",
      "the test loss is (0.179     ): \n",
      "the test loss is (0.138     ): \n",
      "the test loss is (0.084     ): \n",
      "the test loss is (0.167     ): \n",
      "the trainning loss is (0.642     ): \n",
      "the trainning loss is (0.748     ): \n",
      "the trainning loss is (0.774     ): \n",
      "the trainning loss is (0.379     ): \n",
      "the trainning loss is (0.271     ): \n",
      "the trainning loss is (0.157     ): \n",
      "the trainning loss is (0.100     ): \n",
      "the trainning loss is (0.072     ): \n",
      "the trainning loss is (0.050     ): \n",
      "the trainning loss is (0.083     ): \n",
      "the trainning loss is (0.060     ): \n",
      "the trainning loss is (0.055     ): \n",
      "the trainning loss is (0.063     ): \n",
      "the trainning loss is (0.042     ): \n",
      "the trainning loss is (0.037     ): \n",
      "the trainning loss is (0.035     ): \n",
      "the trainning loss is (0.043     ): \n",
      "the trainning loss is (0.025     ): \n",
      "the trainning loss is (0.034     ): \n",
      "the trainning loss is (0.032     ): \n",
      "the trainning loss is (0.025     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.062     ): \n",
      "the trainning loss is (0.036     ): \n",
      "the trainning loss is (0.030     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.048     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.046     ): \n",
      "the trainning loss is (0.024     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.030     ): \n",
      "the trainning loss is (0.028     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.020     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.020     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.023     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the test loss is (3.795     ): \n",
      "the test loss is (4.629     ): \n",
      "the test loss is (2.266     ): \n",
      "the test loss is (1.952     ): \n",
      "the test loss is (1.805     ): \n",
      "the test loss is (0.880     ): \n",
      "the test loss is (0.501     ): \n",
      "the test loss is (0.234     ): \n",
      "the test loss is (0.289     ): \n",
      "the test loss is (0.263     ): \n",
      "the test loss is (0.324     ): \n",
      "the test loss is (0.173     ): \n",
      "the test loss is (0.254     ): \n",
      "the test loss is (0.387     ): \n",
      "the test loss is (0.314     ): \n",
      "the test loss is (0.098     ): \n",
      "the test loss is (0.055     ): \n",
      "the test loss is (0.102     ): \n",
      "the test loss is (0.198     ): \n",
      "the test loss is (0.084     ): \n",
      "the test loss is (0.300     ): \n",
      "the test loss is (0.136     ): \n",
      "the test loss is (0.158     ): \n",
      "the test loss is (0.204     ): \n",
      "the trainning loss is (0.610     ): \n",
      "the trainning loss is (0.579     ): \n",
      "the trainning loss is (0.623     ): \n",
      "the trainning loss is (0.250     ): \n",
      "the trainning loss is (0.141     ): \n",
      "the trainning loss is (0.094     ): \n",
      "the trainning loss is (0.102     ): \n",
      "the trainning loss is (0.061     ): \n",
      "the trainning loss is (0.047     ): \n",
      "the trainning loss is (0.075     ): \n",
      "the trainning loss is (0.043     ): \n",
      "the trainning loss is (0.094     ): \n",
      "the trainning loss is (0.037     ): \n",
      "the trainning loss is (0.037     ): \n",
      "the trainning loss is (0.103     ): \n",
      "the trainning loss is (0.037     ): \n",
      "the trainning loss is (0.044     ): \n",
      "the trainning loss is (0.022     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.024     ): \n",
      "the trainning loss is (0.030     ): \n",
      "the trainning loss is (0.061     ): \n",
      "the trainning loss is (0.036     ): \n",
      "the trainning loss is (0.025     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.031     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.025     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.029     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.029     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.004     ): \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.003     ): \n",
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.024     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the test loss is (1.290     ): \n",
      "the test loss is (0.877     ): \n",
      "the test loss is (0.593     ): \n",
      "the test loss is (0.385     ): \n",
      "the test loss is (0.440     ): \n",
      "the test loss is (0.236     ): \n",
      "the test loss is (0.119     ): \n",
      "the test loss is (0.091     ): \n",
      "the test loss is (0.061     ): \n",
      "the test loss is (0.080     ): \n",
      "the test loss is (0.060     ): \n",
      "the test loss is (0.138     ): \n",
      "the test loss is (0.057     ): \n",
      "the test loss is (0.056     ): \n",
      "the test loss is (0.077     ): \n",
      "the test loss is (0.057     ): \n",
      "the test loss is (0.043     ): \n",
      "the test loss is (0.035     ): \n",
      "the test loss is (0.044     ): \n",
      "the test loss is (0.021     ): \n",
      "the test loss is (0.036     ): \n",
      "the test loss is (0.038     ): \n",
      "the test loss is (0.037     ): \n",
      "the test loss is (0.049     ): \n",
      "the trainning loss is (0.319     ): \n",
      "the trainning loss is (0.499     ): \n",
      "the trainning loss is (0.315     ): \n",
      "the trainning loss is (0.259     ): \n",
      "the trainning loss is (0.124     ): \n",
      "the trainning loss is (0.101     ): \n",
      "the trainning loss is (0.159     ): \n",
      "the trainning loss is (0.125     ): \n",
      "the trainning loss is (0.108     ): \n",
      "the trainning loss is (0.039     ): \n",
      "the trainning loss is (0.061     ): \n",
      "the trainning loss is (0.058     ): \n",
      "the trainning loss is (0.038     ): \n",
      "the trainning loss is (0.036     ): \n",
      "the trainning loss is (0.025     ): \n",
      "the trainning loss is (0.041     ): \n",
      "the trainning loss is (0.034     ): \n",
      "the trainning loss is (0.025     ): \n",
      "the trainning loss is (0.063     ): \n",
      "the trainning loss is (0.022     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.043     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.065     ): \n",
      "the trainning loss is (0.034     ): \n",
      "the trainning loss is (0.073     ): \n",
      "the trainning loss is (0.062     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.063     ): \n",
      "the trainning loss is (0.027     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.034     ): \n",
      "the trainning loss is (0.041     ): \n",
      "the trainning loss is (0.023     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.037     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.029     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.020     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.008     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.031     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.030     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.019     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the test loss is (1.977     ): \n",
      "the test loss is (1.302     ): \n",
      "the test loss is (0.804     ): \n",
      "the test loss is (0.857     ): \n",
      "the test loss is (0.504     ): \n",
      "the test loss is (0.344     ): \n",
      "the test loss is (0.186     ): \n",
      "the test loss is (0.179     ): \n",
      "the test loss is (0.070     ): \n",
      "the test loss is (0.085     ): \n",
      "the test loss is (0.079     ): \n",
      "the test loss is (0.134     ): \n",
      "the test loss is (0.050     ): \n",
      "the test loss is (0.063     ): \n",
      "the test loss is (0.106     ): \n",
      "the test loss is (0.071     ): \n",
      "the test loss is (0.059     ): \n",
      "the test loss is (0.032     ): \n",
      "the test loss is (0.058     ): \n",
      "the test loss is (0.030     ): \n",
      "the test loss is (0.052     ): \n",
      "the test loss is (0.066     ): \n",
      "the test loss is (0.058     ): \n",
      "the test loss is (0.083     ): \n"
     ]
    }
   ],
   "source": [
    "print('==> Training parameters:')\n",
    "print('        start_epoch = {}'.format(start_epoch+1))\n",
    "print('        lr @epoch=0 = {}'.format(0.1))\n",
    "print('==> Starting training...')\n",
    "for epoch in range(0, 5):\n",
    "    if epoch>start_epoch:\n",
    "        train(epoch, net)\n",
    "        test(epoch, net)\n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0639,  0.4956,  3.3795, -0.3755]], device='cuda:0',\n",
       "       grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = Image.open('/home/colin/results/7900.jpg')\n",
    "image = image.resize((320,240))\n",
    "plt.imshow(image)\n",
    "image_tensor = transform(image)\n",
    "image_tensor = image_tensor.view(1,3,240,320)\n",
    "output = net(image_tensor)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:deep_uncertainty]",
   "language": "python",
   "name": "conda-env-deep_uncertainty-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
